scholarly journals The Neural Network Analysis of Normality of Small Samples of Biometric Data through Using the Chi-Square Test and Anderson–Darling Criteria

2019 ◽  
pp. 205-217
Author(s):  
Vladimir I. Volchikhin ◽  
Aleksandr I. Ivanov ◽  
Alexander V. Bezyaev ◽  
Evgeniy N. Kupriyanov
Keyword(s):  
Neural Network ◽  
Sample Size ◽  
Single Layer ◽  
Test Sample ◽  
Small Samples ◽  
Chi Square ◽  
Biometric Data ◽  
The Neural Network ◽  
Anderson Darling ◽  
Statistical Criteria

Introduction. The aim of the work is to reduce the requirements to test sample size when testing the hypothesis of normality. Materials and Methods. A neural network generalization of three well-known statistical criteria is used: the chi-square criterion, the Anderson–Darling criterion in ordinary form, and the Anderson–Darling criterion in logarithmic form. Results. The neural network combining of the chi-square criterion and the Anderson–Darling criterion reduces the sample size requirements by about 40 %. Adding a third neuron that reproduces the logarithmic version of the Andersоn–Darling test leads to a small decrease in the probability of errors by 2 %. The article deals with single-layer and multilayer neural networks, summarizing many currently known statistical criteria. Discussion and Conclusion. An assumption has been made that an artificial neuron can be assigned to each of the known statistical criteria. It is necessary to change the attitude to the synthesis of new statistical criteria that previously prevailed in the 20th century. There is no current need for striving to create statistical criteria for high power. It is much more advantageous trying to ensure that the data of newly synthesized statistical criteria are low correlated with many of the criteria already created.

scholarly journals Neural network integration of classical statistical tests for processing small samples of biometrics data

Dependability ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 22-27 ◽  
Author(s):  
A. I. Ivanov ◽  
E. N. Kuprianov ◽  
S. V. Tureev
Keyword(s):  
Neural Network ◽  
Statistical Tests ◽  
Pair Correlation ◽  
Test Sample ◽  
Small Samples ◽  
Network Integration ◽  
Chi Square ◽  
Biometric Data ◽  
Joint Application ◽  
Artificial Neurons

The Aim of this paper is to increase the power of statistical tests through their joint application to reduce the requirement for the size of the test sample. Methods. It is proposed to combine classical statistical tests, i.e. chi square, Cram r-von Mises and Shapiro-Wilk by means of using equivalent artificial neurons. Each neuron compares the input statistics with a precomputed threshold and has two output states. That allows obtaining three bits of binary output code of a network of three artificial neurons. Results. It is shown that each of such criteria on small samples of biometric data produces high values of errors of the first and second kind in the process of normality hypothesis testing. Neural network integration of three tests under consideration enables a significant reduction of the probabilities of errors of the first and second kind. The paper sets forth the results of neural network integration of pairs, as well as triples of statistical tests under consideration. Conclusions. Expected probabilities of errors of the first and second kind are predicted for neural network integrations of 10 and 30 classical statistical tests for small samples that contain 21 tests. An important element of the prediction process is the symmetrization of the problem, when the probabilities of errors of the first and second kind are made identical and averaged out. Coefficient modules of pair correlation of output states are averaged out as well by means of artificial neuron adders. Only in this case the connection between the number of integrated tests and the expected probabilities of errors of the first and second kind becomes linear in logarithmic coordinates.

scholarly journals The approach to the formation of the neural network architecture for pattern recognition

2016 ◽  
Vol 12 (2) ◽  
pp. 61-64 ◽  
Author(s):  
Vitaly M Tatyankin
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Architecture ◽  
Test Sample ◽  
Recognition Algorithm ◽  
Neural Network Architecture ◽  
Pattern Recognition Algorithm ◽  
The Neural Network

An approach to the formation of an efficient pattern recognition algorithm. Under efficiency, understood as a zero error, resulting in the identification of the images on the test sample. As a test sample is considered an open database of images of handwritten digits MNIST.

scholarly journals Authenticated Encryption Based on Chaotic Neural Networks and Duplex Construction

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2432
Author(s):  
Nabil Abdoun ◽  
Safwan El Assad ◽  
Thang Manh Hoang ◽  
Olivier Deforges ◽  
Rima Assaf ◽  
...  
Keyword(s):  
Neural Network ◽  
Statistical Tests ◽  
Single Layer ◽  
Linear Functions ◽  
Authenticated Encryption ◽  
Chaotic Neural Network ◽  
Chi Square ◽  
Chaotic Neural Networks ◽  
Two Phases ◽  
Associated Data

In this paper, we propose, implement and analyze an Authenticated Encryption with Associated Data Scheme (AEADS) based on the Modified Duplex Construction (MDC) that contains a chaotic compression function (CCF) based on our chaotic neural network revised (CNNR). Unlike the standard duplex construction (SDC), in the MDC there are two phases: the initialization phase and the duplexing phase, each contain a CNNR formed by a neural network with single layer, and followed by a set of non-linear functions. The MDC is implemented with two variants of width, i.e., 512 and 1024 bits. We tested our proposed scheme against the different cryptanalytic attacks. In fact, we evaluated the key and the message sensitivity, the collision resistance analysis and the diffusion effect. Additionally, we tested our proposed AEADS using the different statistical tests such as NIST, Histogram, chi-square, entropy, and correlation analysis. The experimental results obtained on the security performance of the proposed AEADS system are notable and the proposed system can then be used to protect data and authenticate their sources.

scholarly journals THE NEURAL NETWORK MODEL OF INDIVIDUALS CREDIT RATING

2019 ◽  
Vol 8 (6) ◽  
Author(s):  
Ilyas I. Ismagilov ◽  
Linar A. Molotov ◽  
Alexey S. Katasev ◽  
Dina V. Kataseva
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Credit Rating ◽  
Test Sample ◽  
Training Sample ◽  
Consumer Lending ◽  
The Neural Network ◽  
Intelligent Decision Support Systems ◽  
Model Training

This article solves the problem of constructing and evaluating a neural network model to determine the creditworthiness of individuals. It is noted that the most important part of the modern retail market is consumer lending. Therefore, an adequate and high-quality assessment of the creditworthiness of an individual is a key aspect of providing credit to a potential borrower. The theoretical and practical aspects of assessing the creditworthiness of individuals are considered. To solve this problem, the need for the use of intelligent modeling technologies based on neural networks is being updated. The construction of a neural network model required the receipt of initial data on borrowers. Using correlation analysis, 14 input parameters were selected that most significantly affect the output. The training and test data samples were generated to build and evaluate the adequacy of the neural network model. Training and testing of the neural network model was carried out on the basis of the analytical platform “Deductor”. Analysis of contingency tables to assess the accuracy of the neural network model in the training and test samples showed positive results. The error of the first kind on the data from the training sample was 0.45%, and the error of the second kind was 1.39%. Accordingly, the error of the first kind was not observed on the data from the test sample, and the error of the second kind was 2.68%. The results obtained indicate a high generalizing ability and adequacy of the constructed neural network, as well as the possibility of its effective practical use as part of intelligent decision support systems for granting loans to potential borrowers

scholarly journals Precision Statistics: Fractional Number of Degrees of Freedom Chi-Square Criterion for Small Samples of Biometric Data

2019 ◽  
Vol 6 (1) ◽  
pp. 55-62
Author(s):  
V.I. Volchikhin ◽  
◽  
A.I. Ivanov ◽  
E.A. Malygina ◽  
E.N. Kupriyanov ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Small Samples ◽  
Chi Square ◽  
Biometric Data ◽  
Fractional Number

scholarly journals DEVELOPMENT AND VALIDATION OF OPHTHALMIC PATHOLOGY CLASSIFICATION METHOD USING DEEP MACHINE LEARNING

2021 ◽  
Vol 2021 (2) ◽  
pp. 57-65
Author(s):  
Vadim I. Loshmanov ◽  
Alla Kravets
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Information Technologies ◽  
System Development ◽  
Test Sample ◽  
Universal Method ◽  
Laboratory Studies ◽  
Preclinical Research ◽  
The Neural Network ◽  
Ophthalmic Pathology

The article highlights the problem of improving the numerous practical methods of developing the new chemical compounds that can be used in pharmacology. One of the possible ways to introduce innovative methods into conducting preclinical drug tests is the application of ac-tively developing information technologies, such as data mining with methods of deep machine learning. With a huge amount of data accumulated over several years of preclinical research, the practical solutions in this area allow to obtain a neural network data model with greater degree of accuracy, though, there is no universal method that would allow a comprehensive approach to the problem of analyzing the results of preclinical laboratory studies of drugs. Existing solutions have a few disadvantages, which prevents from using them in practice. The two main problems are: the difficulty in verifying the results and the incompleteness of the list of calculated parameters. A system of identifying the pharmacological activity of a new drug is proposed to solve the problem, which was considered on the example of ophthalmic preclinical laboratory studies. As part of the system development, a method for classifying ophthalmic pathology based on a convolutional neural network has been implemented. The architecture of the neural network has been developed, its hyperparameters being matched experimentally. The model accuracy during training made 90%, and the test sample accuracy made 81%.

The Influence of Outliers on Discrimination of Chronic Obturative Lung Disease

1988 ◽  
Vol 27 (04) ◽  
pp. 167-176 ◽  
Author(s):  
Ewa Krusmska ◽  
Jerzy Liebhart
Keyword(s):  
Lung Disease ◽  
Sample Size ◽  
Test Data ◽  
Medical Diagnosis ◽  
Respiratory Diseases ◽  
Test Sample ◽  
Training Sample ◽  
Small Samples

SummaryThe paper discusses the influence of outliers on the results of linear and canonical discrimination used to assist medical diagnosis in chronic obturative lung disease. The outliers have been detected by χ2-plots based on unweighted sample means and covariances or their weighted analogues with Huber or Hampel weights. With Hampel weights outliers have been found different from those with both remaining methods. After trimming the 10 percent of the most distant individuals, the discrimination was done for the training sample collected earlier (N′ = 305) and for the test sample (N″ = 53) with the functions obtained from the training sample. The discrimination was performed for subsets of the most discriminative variables. When the sample size was sufficiently large (training sample), the goodness of reclassification was similar for classical functions and functions calculated after trimming. For small samples they differ. For classification of the test data the results obtained after trimming (especially with Hampel weights) are much better. The method may be recommended to be used in the computerized respiratory diseases consulting unit.

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